Fair Machine Learning combats biases

Thema:
Artifical intelligence

An AI tool bases its calculations on data. If the data is biased, the calculations will be biased. If there was once a male preference within a profession, then this will be adopted by AI tools for recruitment. So the AI tool may wrongly give a better judgement to men. This can be prevented by de-correlating the data from gender. Gender and possible related proxies will no longer be predictive for job suitability. TNO expects to use Fair Machine Learning to select appropriate candidates in a fair and unbiased manner.

TNO makes generative adversarial network models using fair machine learning

TNO carries out the de-correlation for Fair Machine Learning using a Generative Adversarial Network (GAN) model. This model tries to balance two conflicting criteria:

  1. Minimising the number of changes to the dataset
  2. Making sure that somebody’s gender is no longer identifiable from the remaining characteristics

When weighing up the criteria, the model generalises the existing characteristics of individuals into more general characteristics. An example would be generalising postcodes according to neighbourhoods, neighbourhoods according to cities and cities according to countries. The end result is a dataset in which a person’s gender (criterion 2) is practically unrecognisable. In short, the gender bias has disappeared from the dataset.

Fair machine learning is relevant to all forms of discrimination arising from historical data

Fair Machine Learning is relevant to all forms of discrimination and prejudice that arise from the use of biased data. In addition to recruitment and selection, it is also important that the AI algorithm is fair when it comes to supervision, inspection and enforcement tasks. Gender, religion and ethnicity should not be used as selection characteristics.

If used responsibly, AI machine learning tools can increase efficiency and effectiveness when finding comparable individuals for all kinds of selection tasks. However, historical biases (which are less striking without these AI tools) are being structurally and systematically furthered by them. Fair Machine Learning reduces and prevents such discrimination.

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Educating AI

Informatietype:
Insight
27 September 2022

You can read about how AI is educated in Chapter 1. How can we make clear to AI which goals we want to pursue as humans? Andhow can we ensure intelligent systems will always function in service of society?

Innovation with AI

Informatietype:
Insight
27 September 2022

What does that world look like in concrete terms? Using numerous examples, TNO has created a prognosis for the future in Chapter 2. Regarding construction, for example, in which AI will be used to check the quality, safety, and energy efficiency of buildings before they are actually built. Or healthcare, where robots will partly take over caregivers’ tasks and AI will be able to autonomously develop medicines.

Innovating with innovation AI

Informatietype:
Insight
27 September 2022

How AI will change research itself is explained in Chapter 3. For example, what role will AI be permitted to play in knowledge sharing? And what will happen when we make machines work with insurmountably large data sets?

David Deutsch on the development and application of AI

Informatietype:
Insight
27 September 2022

Peter Werkhoven, chief scientific officer at TNO, joins physicist, Oxford professor, and pioneer in the field of quantum computing, David Deutsch, for a virtual discussion. Deutsch set out his vision in 1997 in the book, The Fabric of Reality. Together, they talk about the significance of quantum computing for the development and application of AI. Will AI ever be able to generate ‘explained knowledge’ or learn about ethics from humans?

Rob de Wijk on the rise of AI in geopolitical context

Informatietype:
Insight
27 September 2022

Anne Fleur van Veenstra, director of science at TNO’s SA&P unit, interviews Rob de Wijk, emeritus professor of international relations in Leiden and founder of The Hague Centre for Strategic Studies. Rob is also a much sought-after expert who appears on radio and television programmes. What does the rise of AI mean geopolitically and in armed conflicts?